When rebuilding rpy2 after commenting out line 106 in setup.py:
define_macros.append(('RPY_DEBUG_PRESERVE', 1))
I can see that it seems that R objects are
preserved but not released until the termination of the python process.
In fact during each iteration I have 6 R objects preserved, but only 4
released - the release of the two last ones is deferred to the
termination of the python process. The question now is "why".
Laurent
laurent oget wrote:
> I have been able to improve the leak considerably with your suggestion
> and also attaching the dataFrame instead of passing it as an argument
> of lm every time. As an added benefit this reduces the number of copy,
> which increases performance. It is still leaking, though....
>
> Laurent
>
> 2008/11/21 Laurent Gautier <[EMAIL PROTECTED]>:
>> laurent oget wrote:
>>> is there something i can do to prevent the following script from leaking
>>> memory?
>>
>> There seems to be something happening.
>> (more below)
>>
>>> Laurent
>>>
>>> from rpy2.robjects import r,RVector,RDataFrame
>>> from rpy2.rlike.container import TaggedList
>>> from math import sin
>>> import array
>>>
>>> size=100
>>> xx=[]
>>> for x in range(size):
>>> xx.append(RVector(array.array('f',[sin(0.1*(y+x))for y in
>>> range(1000)])))
>>>
>>
>> (side note: the line above could write easier
>>
>> from rpy2.robjects import FloatVector
>>
>> for x in range(size):
>> xx.append(FloatVector([sin(0.1 * (x+y) for y in range(1000)]))
>> )
>>
>>
>>> formula='x%s~%s'%(0,'+'.join(['x%d'%(i+1) for i in range(size-1)]))
>>>
>>> t=['x%d'%i for i in range(size)]
>>> df=RDataFrame(TaggedList(xx,tags=t))
>>>
>>> done=False
>>> i=0
>>> while not done:
>>> print i
>>> i=i+1
>>> model=r.lm(formula,df)
>> I thought that the following would solve the problem... but it does not.
>> The increase in running speed suggests that this is because there is less
>> copying (therefore the problem about copying and protecting R objects and
>> never unprotecting them). The growth of the process is slower, but still
>> there...
>>
>>
>> from rpy2.robjects import globalEnv
>>
>> globalEnv["dataf"] = df
>>
>> done=False
>> i=0
>> while not done:
>> print i
>> i=i+1
>> model=r('lm("%s", data=dataf)' %formula)
>>
>>
>> The problem will likely require a closer look.
>>
>>
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